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Record W3196330023 · doi:10.1115/1.4052275

A Surface Energy Approach to Developing an Analytical Model for the Underfill Flow Process in Flip-Chip Packaging

2021· article· en· W3196330023 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Electronic Packaging · 2021
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Science Foundation of Shanghai
KeywordsFlip chipSolderingMechanical engineeringFlow (mathematics)ChipProcess (computing)Electronic packagingMaterials scienceIntegrated circuit packagingThermal copper pillar bumpElectronic engineeringComputer scienceEngineeringMechanicsElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract This paper presents a new approach to formulating an analytical model for the underfill process in flip-chip packaging to predict the flow front and the filling time. The new approach is based on the concept of surface energy along with the energy conservation principle. This approach avoids the need of modeling the flow path to predict the flow front and the filling time, and thus it is suitable to different configurations of solder bumps, including different shapes and arrangements of solder bumps in flip-chip packaging. An experiment along with the computational fluid dynamics simulation was performed based on a proprietarily developed testbed to verify the effectiveness of this approach. Both the experimental and simulation results show that the proposed approach along with its model is accurate for flip-chip packages with different configurations besides the configuration of a regular triangle arrangement of solder bumps and a spherical shape of the solder bump.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.280
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it